Systemic Risk Indicators Based on Nonlinear PolyModel

Ye, Xingxing; Douady, Raphaël

doi:10.3390/jrfm12010002

Cited by 7 publications

(4 citation statements)

References 33 publications

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“…If they were to be used as fixed regulatory requirements over time, during periods of financial sector exuberance, when the market typically under-prices risk and overvalues firm equity, they might be prone to underestimating the share of banks that might become stressed when the cycle turns. We see evidence consistent with this in Figure X, where we compare the 2005 and 2006 data: on average, the market-based RWCR and LR were higher in 2006 than in 2005, despite increasing risks in the lead-up to the crisis (Ye and Douady, 2018). By comparison, the regulatory metrics based on banks' balance sheets were more stable.…”

Section: Market-based Metricssupporting

confidence: 68%

The More the Merrier? Evidence from the Global Financial Crisis on the Value of Multiple Requirements in Bank Regulation

et al. 2021

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This paper assesses the value of multiple requirements in bank regulation using a novel empirical rule-based methodology. Exploiting a dataset of capital and liquidity ratios for a sample of global banks in 2005 and 2006, we apply simple threshold-based rules to assess how different regulations individually and in combination might have identified banks that subsequently failed during the global financial crisis. Our results generally support the case for a small portfolio of different regulatory metrics. Under the objective of correctly identifying a high proportion of banks which subsequently failed, we find that a portfolio of a leverage ratio, a risk-weighted capital ratio, and a net stable funding ratio yields fewer false alarms than any of these metrics individually-and at less stringent calibrations of each individual regulatory metric. We also discuss how these results apply in different robustness exercises, including out-of-sample evaluations. Finally, we consider the potential role of market-based measures of bank capitalisation, showing that they provide complementary value to their accounting-based counterparts.

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Section: Market-based Metricssupporting

confidence: 68%

The More the Merrier? Evidence from the Global Financial Crisis on the Value of Multiple Requirements in Bank Regulation

et al. 2021

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“…Different types of factors capture the changes of market behaviors caused by different sources. The factors we use are from Ye and Douady [9]. We downloaded the data from Bloomberg.…”

Section: Datamentioning

confidence: 99%

Has the Market Started to Collapse or Will It Resist?

Kuang

Douady

2022

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Many people are concerned about the stock market in 2022 as it faces several threats, from rising inflation rates to geopolitical events. The S&P 500 Index has already dropped about 10% from the peak in early January 2022 until the end of February 2022. This paper aims at updating the crisis indicator to predict when the market may experience a significant drawdown, which we developed in Crisis Risk Prediction with Concavity from Polymodel (2022). This indicator uses regime switching and Polymodel theory to calculate the market concavity. We found that concavity had not increased in the past 6 months. We conclude that at present, the market does not bear inherent dynamic instability. This does not exclude a possible collapse which would be due to external events unrelated to financial markets.

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“…Therefore, in terms of theoretical models, this article constructs a GARCH EVT-Copula function hybrid model based on Gumbel Copula, Clayton Copula, and Generalized Autoregressive Score (GARCH EVT) models [10][11][12][13][14]. This model can mix different types of Copulas to more accurately capture the dependent structures of financial time series.…”

Section: Introductionmentioning

confidence: 99%

Industry Systemic Risk Measurement Based on GARCH-EVT Method and Copula Function

Ding,

Wang,

Chen

2024

Proceedings of the 5th International Conference on Economic Management and Model Engineering, ICEMME 2023, November 17–19, 2023

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The article uses data from the Shanghai and Shenzhen 300 Index and the Shenwanwan Industry Index from 2010 to 2021, constructs a dynamic weighted mixed Copula model based on GARCH EVT, analyzes the dependence between each industry and the market tail, and explores the systematic risk contribution of each industry based on marginal expected loss (MES). The empirical results indicate that industries such as agriculture, forestry, animal husbandry, and fishing, as well as non banking and finance, have relatively low tail dependence, while industries such as real estate and household appliances have relatively high tail dependence; The banking industry has the smallest contribution to systemic risk, while the construction materials industry has the largest contribution; During the 2015 "stock market crash", the tail dependence between industry indices such as real estate and mining and market indices increased, with the most significant increase in risk contribution from the defense, military, and chemical industries.

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Systemic Risk Indicators Based on Nonlinear PolyModel

Cited by 7 publications

References 33 publications

The More the Merrier? Evidence from the Global Financial Crisis on the Value of Multiple Requirements in Bank Regulation

The More the Merrier? Evidence from the Global Financial Crisis on the Value of Multiple Requirements in Bank Regulation

Has the Market Started to Collapse or Will It Resist?

Industry Systemic Risk Measurement Based on GARCH-EVT Method and Copula Function

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